26 citations · 97 across the 13 of their papers we have counts for
6 papers · 1 filter
Supercharging Graph Transformers with Advective Diffusion
Qitian Wu, Chenxiao Yang, Kaipeng Zeng +1
The capability of generalization is a cornerstone for the success of modern learning systems. For non-Euclidean data, e.g., graphs, that particularly involves topological structure…
How Graph Neural Networks Learn: Lessons from Training Dynamics
Chenxiao Yang, Qitian Wu, David Wipf +2
A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considera…
GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks
Wentao Zhao, Qitian Wu, Chenxiao Yang +1
Graph structure learning is a well-established problem that aims at optimizing graph structures adaptive to specific graph datasets to help message passing neural networks (i.e., G…
SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
Qitian Wu, Wentao Zhao, Chenxiao Yang +5
Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points. Transformers, as an emerging class o…
Energy-based Out-of-Distribution Detection for Graph Neural Networks
Qitian Wu, Yiting Chen, Chenxiao Yang +1
Learning on graphs, where instance nodes are inter-connected, has become one of the central problems for deep learning, as relational structures are pervasive and induce data inter…
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion
Qitian Wu, Chenxiao Yang, Wentao Zhao +3
Real-world data generation often involves complex inter-dependencies among instances, violating the IID-data hypothesis of standard learning paradigms and posing a challenge for un…